Trends of Molecular Testing for Lung Cancer at the King Faisal Hospital, Kigali: Therapeutic and Survival Implications
Bibliographic record
Abstract
Introduction Lung cancer is the leading cause of cancer mortality worldwide, both in high and low resource settings. Knowledge has been generated elsewhere regarding molecular subtyping and subsequent targeted therapy development, contributing substantially to patient survival. Little is known on the data around lung cancer and its treatment outcomes in Sub-Saharan Africa. This study describes the experience in lung cancer diagnosis, molecular and biomarker testing, and treatment for advanced cases in a single institution in East Africa, between the years 2019 and 2021. Methods This was a retrospective observational study evaluating patients with metastatic (stage IV) lung cancer. Data on patient demographics, histologic diagnosis, molecular and biomarker testing, and treatment details and outcomes were collected. Molecular test results were reported as positive if there were biomarkers identified (e.g., EGFR , ALK , programmed death-ligand 1), and patients who had negative test results were reported as negative for biomarkers. Results A total of 14 patients were diagnosed with having stage IV disease, and all were proposed to undergo molecular testing. For 12 (86%) patients who were able to have molecular testing done, EGFR and programmed death-ligand 1 were the most common with 66.7% ( N = 8) of tissues with either finding. For all 14 patients, treatment changes were made for eight patients (57.1%) after being primarily placed on a combination of paclitaxel and carboplatin for an average of six cycles. Changing treatment significantly improved the 2-year overall survival (85% versus 25%, p = 0.0006). Conclusions Despite being the number one cause of mortality, gains are being made in poor-resource settings to improve the survival of patients with advanced lung cancers. Limitations to this quest remain misdiagnosis and delayed diagnosis and resource constraints for both molecular testing and subsequent treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".